利用质子能谱和微剂量学模型来模拟质子相对生物学效应。
Using the Proton Energy Spectrum and Microdosimetry to Model Proton Relative Biological Effectiveness.
机构信息
Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas; Medical Physics Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas.
Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
出版信息
Int J Radiat Oncol Biol Phys. 2019 Jun 1;104(2):316-324. doi: 10.1016/j.ijrobp.2019.01.094. Epub 2019 Feb 5.
PURPOSE
We introduce a methodology to calculate the microdosimetric quantity dose-mean lineal energy for input into the microdosimetric kinetic model (MKM) to model the relative biological effectiveness (RBE) of proton irradiation experiments.
METHODS AND MATERIALS
The data from 7 individual proton RBE experiments were included in this study. In each experiment, the RBE at several points along the Bragg curve was measured. Monte Carlo simulations to calculate the lineal energy probability density function of 172 different proton energies were carried out with use of Geant4 DNA. We calculated the fluence-weighted lineal energy probability density function (f(y)), based on the proton energy spectra calculated through Monte Carlo at each experimental depth, calculated the dose-mean lineal energy y¯ for input into the MKM, and then computed the RBE. The radius of the domain (r) was varied to reach the best agreement between the MKM-predicted RBE and experimental RBE. A generic RBE model as a function of dose-averaged linear energy transfer (LET) with 1 fitting parameter was presented and fit to the experimental RBE data as well to facilitate a comparison to the MKM.
RESULTS
Both the MKM and LET-based models modeled the RBE from experiments well. Values for r were similar to those of other cell lines under proton irradiation that were modeled with the MKM. Analysis of the performance of each model revealed that neither model was clearly superior to the other.
CONCLUSIONS
Our 3 key accomplishments include the following: (1) We developed a method that uses the proton energy spectra and lineal energy distributions of those protons to calculate dose-mean lineal energy. (2) We demonstrated that our application of the MKM provides theoretical validation of proton irradiation experiments that show that RBE is significantly greater than 1.1. (3) We showed that there is no clear evidence that the MKM is better than LET-based RBE models.
目的
我们介绍了一种计算微剂量学量剂量平均线性能量的方法，以便将其输入到微剂量动力学模型（MKM）中，从而对质子辐照实验的相对生物效应（RBE）进行建模。
方法和材料
本研究纳入了 7 项单独的质子 RBE 实验的数据。在每项实验中，均在布喇格曲线的多个点测量 RBE。使用 Geant4 DNA 进行蒙特卡罗模拟，以计算 172 种不同质子能量的线性能量概率密度函数。我们基于在每个实验深度通过蒙特卡罗计算得出的质子能谱，计算了线性能量的剂量加权概率密度函数（f(y)），计算了输入到 MKM 的剂量平均线性能量 y¯，然后计算了 RBE。改变域半径（r）以达到 MKM 预测的 RBE 与实验 RBE 之间的最佳一致性。提出了一个通用的 RBE 模型，作为剂量平均线性能量转移（LET）的函数，具有 1 个拟合参数，并拟合实验 RBE 数据，以便与 MKM 进行比较。
结果
MKM 和基于 LET 的模型都很好地模拟了实验中的 RBE。r 的值与使用 MKM 建模的质子辐照下其他细胞系的值相似。对每种模型性能的分析表明，没有一种模型明显优于另一种模型。
结论
我们的 3 个主要成果包括：（1）我们开发了一种方法，该方法使用质子能谱和这些质子的线性能量分布来计算剂量平均线性能量。（2）我们证明了我们对 MKM 的应用为显示 RBE 明显大于 1.1 的质子辐照实验提供了理论验证。（3）我们表明，没有明显的证据表明 MKM 优于基于 LET 的 RBE 模型。
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